CA studies of assessments as distinct, sequentially organized social actions (Pomerantz, 1984) have tended to define assessments for the purposes of data selection (Ogden, 2006, p. 1758) as “utterances that offer an evaluation of a referent with a clear valence” (Stivers & Rossano, 2010). However, this definition may exclude evaluative practices where the ‘valenced’ terms of assessment are more equivocal. It also obscures how the valences that mark out an utterance as an assessment are produced interactionally in the first place. This paper follows Goodwin & Goodwin’s (1992) proposal that assessment ‘segments’ (words like ‘good’ or ‘beautiful’), and assessment ‘signals’ (vocalizations like “mmm!” or “ugh!”) are organized into sequential ‘slots’ that render both ‘segments’ and ‘signals’ reflexively accountable as evaluative ‘assessment activities’. Data are drawn from recordings of a novice partner dance workshop at moments where teachers’ pro-forma terminal assessments marking the completion of a dance practice session co-occur with students’ evaluative assessment activities. Analysis shows how students use non-lexical vocalizations as evaluative assessments after imitating the bodily-vocal demonstrations (Keevalik, 2014) of the teachers and completing an unfamiliar dance move together. Extract 1 shows one example of these non-lexical vocalizations as dance partners Paul and Mary complete a new dance movement while the teachers call out rhythms and instructions.

The analysis suggests that non-lexical vocalizations provide a useful resource for evaluating the achievement of as-yet-unfamiliar joint actions and managing and calibrating subtle degrees and dimensions of individual and mutual accountability for troubles encountered in learning a new, unfamiliar partner dance movement.

Mick Smith and I are organizing this panel on noticings at ICCA 2018. We’re really excited to have submissions from some amazing EM/CA scholars to help us explore this questions of action formation / ascription, embodiment, multiactivity, and reference across at least three languages.

Noticings as actions-in-conversation are a ubiquitous, versatile, but under-researched phenomenon (Keisanen, 2012). Schegloff (2007b, p. 218) suggests that noticings “put on offer a line of talk” that renders something optionally relevant for subsequent interaction, although Stivers & Rossano’s (2010) study of the diminished ‘response-relevance’ of noticings leads some analysts to question whether noticings function as social actions (Thompson, Fox, & Couper-Kuhlen, 2015, p. 141) formed from prospectively paired ‘action types’ (Levinson, 2013), or whether they are organised—as Schegloff (2007b, p. 219) suggests—as a generic retro-sequence pointing backwards to a prior ‘noticeable’. Alongside these debates, C. Goodwin & Goodwin (2012) focus on how noticings point “outside of talk”, drawing as-yet-unnoticed resources into embodied social action. Without pre-specifying any one analytic characterization, this panel brings together research explores the ambiguities of noticings as social actions alongside a range of mobile and embodied practices where describing (Sidnell & Barnes, 2009), referring (Hindmarsh & Heath, 2000), and categorizing may also be at issue (Schegloff, 2007a). Alongside empirical studies, contributors also address theoretical questions that arise from treating noticings as conversational devices. How are researchers’ noticings and participants’ noticings differently constitutive of interactional phenomena (Laurier, 2013)? Do noticings emerge reflexively as part of a particular interactional environment and work towards particular interactional ends (Schegloff, 2007a, p. 87 note 17), or are analytic invocations of ‘noticing’ in CA flawed descriptions that obscure more of the action than they clarify? Drawing together diverse approaches to noticings, this panel asks how understanding noticings as actions-in-conversation may open up new empirical and theoretical questions and challenges.

Here’s the abstract to an ICCA 2018 paper I’m working on with J.P. de Ruiter at the Human Interaction Lab at Tufts. The goal is to use computational linguistic methods (that often use the term ‘backchannel’) to see if all these responsive particles really belong in one big undifferentiated ‘bucket’.

Many studies of dialogue use the catch-all term ‘backchannel’ (Yngve ,1970) to refer to a wide range of utterances and behaviors as forms of listener-feedback in interaction. The use of this wide category ignores nearly half a century of research into the highly differentiated interactional functions of ‘continuers’ such as ‘uh huh’ or ‘wow’ (Schegloff, 1982, Goodwin, 1986), acknowledgement tokens such as ‘yeah’, ‘right’ or ‘okay’ (Jefferson, 1984; Beach, 1993) and change-of-state markers such as ‘oh’ or ‘nå’ (Heritage, 1984; Heinemann, 2017). These studies show how participants use responsive particles as fully-fledged, individuated, and distinctive words that do not belong in an undifferentiated functional class of ‘backchannels’ (Sorjonen, 2001). For this paper we use the Conversation Analytic British National Corpus (CABNC) (Albert, L. de Ruiter & J. P. de Ruiter, 2015) – a 4.2M word corpus featuring audio recordings of interaction from a wide variety of everyday settings that facilitates ‘crowdsourced’ incremental improvements and multi-annotator coding. We use Bayesian model comparison to evaluate the relative predictive performance of two competing models. In the first of these, all ‘backchannels’ imply the same amount of floor-yielding, while the second CA informed model assumes that different response tokens are more or less effective in ushering extended turns or sequences to a close. We argue that using large corpora together with statistical models can also identify candidate ‘deviant cases’, providing new angles and opportunities for ongoing detailed, inductive conversation analysis. We discuss the methodological implications of using “big data” with CA, and suggest key guidelines and common pitfalls for researchers using large corpora and statistical methods at the interface between CA and cognitive psychology (De Ruiter & Albert, 2017).